ABSTRACT
To correlate the variable clinical features of oestrogen-receptor-positive breast cancer with somatic alterations, we studied pretreatment tumour biopsies accrued from patients in two studies of neoadjuvant aromatase inhibitor therapy by massively parallel sequencing and analysis. Eighteen significantly mutated genes were identified, including five genes (RUNX1, CBFB, MYH9, MLL3 and SF3B1) previously linked to haematopoietic disorders. Mutant MAP3K1 was associated with luminal A status, low-grade histology and low proliferation rates, whereas mutant TP53 was associated with the opposite pattern. Moreover, mutant GATA3 correlated with suppression of proliferation upon aromatase inhibitor treatment. Pathway analysis demonstrated that mutations in MAP2K4, a MAP3K1 substrate, produced similar perturbations as MAP3K1 loss. Distinct phenotypes in oestrogen-receptor-positive breast cancer are associated with specific patterns of somatic mutations that map into cellular pathways linked to tumour biology, but most recurrent mutations are relatively infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing.
Subject(s)
Aromatase Inhibitors/therapeutic use , Aromatase/metabolism , Breast Neoplasms/drug therapy , Breast Neoplasms/genetics , Genome, Human/genetics , Anastrozole , Androstadienes/pharmacology , Androstadienes/therapeutic use , Antineoplastic Agents/pharmacology , Antineoplastic Agents/therapeutic use , Breast Neoplasms/metabolism , Breast Neoplasms/pathology , DNA Repair , Exome/genetics , Exons/genetics , Female , Genetic Variation/genetics , Humans , Letrozole , MAP Kinase Kinase 4/genetics , MAP Kinase Kinase Kinase 1/genetics , Mutation/genetics , Nitriles/pharmacology , Nitriles/therapeutic use , Receptors, Estrogen/metabolism , Treatment Outcome , Triazoles/pharmacology , Triazoles/therapeutic useABSTRACT
In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.